Automated Prototype for Bombyx mori Cocoon Sorting Attempts to Improve Silk Quality and Production Efficiency through Multi-Step Approach and Machine Learning Algorithms.

Cocoon sorting is one of the most labor-demanding activities required both at the end of the agricultural production and before the industrial reeling process to obtain an excellent silk quality. In view of the possible relaunch of European sericulture, the automatization of this production step is mandatory both to reduce silk costs and to standardize fiber quality. The described research starts from this criticality in silk production (the manual labor required to divide cocoons into different quality classes) to identify amelioration solutions. To this aim, the automation of this activity was proposed, and a first prototype was designed and built. This machinery is based on the use of three cameras and imaging algorithms identifying the shape and size of the cocoons and outside stains, a custom-made light sensor and an AI model to discard dead cocoons. The current efficiency of the machine is about 80 cocoons per minute. In general, the amelioration obtained through this research involves both the application of traditional sensors/techniques to an unusual product and the design of a dedicated sensor for the identification of dead/alive pupae inside the silk cocoons. A general picture of the overall efficiency of the new cocoon-sorting prototype is also outlined.

Advantages in Using Colour Calibration for Orthophoto Reconstruction.

UAVs are sensor platforms increasingly used in precision agriculture, especially for crop and environmental monitoring using photogrammetry. In this work, light drone flights were performed on three consecutive days (with different weather conditions) on an experimental agricultural field to evaluate the photogrammetric performances due to colour calibration. Thirty random reconstructions from the three days and six different areas of the field were performed. The results showed that calibrated orthophotos appeared greener and brighter than the uncalibrated ones, better representing the actual colours of the scene. Parameter reporting errors were always lower in the calibrated reconstructions and the other quantitative parameters were always lower in the non-calibrated ones, in particular, significant differences were observed in the percentage of camera stations on the total number of images and the reprojection error. The results obtained showed that it is possible to obtain better orthophotos, by means of a calibration algorithm, to rectify the atmospheric conditions that affect the image obtained. This proposed colour calibration protocol could be useful when integrated into robotic platforms and sensors for the exploration and monitoring of different environments.

An Open Source Low-Cost Device Coupled with an Adaptative Time-Lag Time-Series Linear Forecasting Modeling for Apple Trentino (Italy) Precision Irrigation.

Precision irrigation represents those strategies aiming to feed the plant needs following the soil's spatial and temporal characteristics. Such a differential irrigation requires a different approach and equipment with regard to conventional irrigation to reduce the environmental impact and the resources use while maximizing the production and thus profitability. This study described the development of an open source soil moisture LoRa (long-range) device and analysis of the data collected and updated directly in the field (i.e., weather station and ground sensor). The work produced adaptive supervised predictive models to optimize the management of agricultural precision irrigation practices and for an effective calibration of other agronomic interventions. These approaches are defined as adaptive because they self-learn with the acquisition of new data, updating the on-the-go model over time. The location chosen for the experimental setup is a cultivated area in the municipality of Tenna (Trentino, Alto Adige region, Italy), and the experiment was conducted on two different apple varieties during summer 2019. The adaptative partial least squares time-lag time-series modeling, in operative field conditions, was a posteriori applied in the consortium for 78 days during the dry season, producing total savings of 255 mm of irrigated water and 44,000 kW of electricity, equal to 10.82%.

A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots.

The degree of olive maturation is a very important factor to consider at harvest time, as it influences the organoleptic quality of the final product, for both oil and table use. The Jaén index, evaluated by measuring the average coloring of olive fruits (peel and pulp), is currently considered to be one of the most indicative methods to determine the olive ripening stage, but it is a slow assay and its results are not objective. The aim of this work is to identify the ripeness degree of olive lots through a real-time, repeatable, and objective machine vision method, which uses RGB image analysis based on a k-nearest neighbors classification algorithm. To overcome different lighting scenarios, pictures were subjected to an automatic colorimetric calibration method-an advanced 3D algorithm using known values. To check the performance of the automatic machine vision method, a comparison was made with two visual operator image evaluations. For 10 images, the number of black, green, and purple olives was also visually evaluated by these two operators. The accuracy of the method was 60%. The system could be easily implemented in a specific mobile app developed for the automatic assessment of olive ripeness directly in the field, for advanced georeferenced data analysis.

Research Trends and Future Perspectives in Marine Biomimicking Robotics.

Mechatronic and soft robotics are taking inspiration from the animal kingdom to create new high-performance robots. Here, we focused on marine biomimetic research and used innovative bibliographic statistics tools, to highlight established and emerging knowledge domains. A total of 6980 scientific publications retrieved from the Scopus database (1950-2020), evidencing a sharp research increase in 2003-2004. Clustering analysis of countries collaborations showed two major Asian-North America and European clusters. Three significant areas appeared: (i) energy provision, whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy. In the research environment, identifying natural processes by which living organisms obtain energy is thus urgent to sustain energy-demanding tasks while, at the same time, the natural designs must increasingly inform to optimize energy consumption.

Application of near-infrared handheld spectrometers to predict semolina quality.

BACKGROUND: Durum wheat semolina is the best raw material for pasta production and its protein content and gluten strength are essential for cooking quality. The need to develop rapid methods to speed up quality control makes near-infrared spectroscopy (NIR) a useful method that is widely accepted in the cereal sector. In this study, two non-destructive and rapid technologies, a low-cost sensor providing a short wavelength NIR range (swNIR: 700-1100 nm) and a handheld NIR spectrometer (NIR: 1600-2400 nm), were employed to evaluate semolina quality. The spectra data were correlated with chemical (protein content) and rheological parameters (i.e., Gluten Index, Alveograph®, Sedimentation test, GlutoPeak®). A partial least squares (PLS) model was used to compare the efficacy of swNIR and NIR. RESULTS: The protein content was the reference parameter that correlated best with the spectra data and provided the best regression model (r model = 0.9788 for NIR and 0.9561 for swNIR). GlutoPeak indices also correlated well with spectral data, particularly with swNIR spectra. A provisional multivariate model was applied to classify semolina samples in quality classes by using their spectra. Better modeling efficiency was obtained for swNIR. CONCLUSION: The results highlighted the advantages of a pocket-sized low cost sensor (swNIR), which is easier to use directly at the sample source than laboratory instruments or more expensive portable devices. © 2020 Society of Chemical Industry.

Light Drone-Based Application to Assess Soil Tillage Quality Parameters.

The evaluation of soil tillage quality parameters, such as cloddiness and surface roughness produced by tillage tools, is based on traditional methods ranging, respectively, from manual or mechanical sieving of ground samples to handheld rulers, non-contact devices or Precision Agriculture technics, such as laser profile meters. The aim of the study was to compare traditional methods of soil roughness and cloddiness assessment (laser profile meter and manual sieving), with light drone RGB 3D imaging techniques for the evaluation of different tillage methods (ploughed, harrowed and grassed). Light drone application was able to replicate the results obtained by the traditional methods, introducing advantages in terms of time, repeatability and analysed surface while reducing the human error during the data collection on the one hand and allowing a labour-intensive field monitoring solution for digital farming on the other. Indeed, the profilometer positioning introduces errors and may lead to false reading due to limited data collection. Future work could be done in order to streamline the data processing operation and so to produce a practical application ready to use and stimulate the adoption of new evaluation indices of soil cloddiness, such as Entropy and the Angular Second Moment (ASM), which seem more suitable than the classic ones to achieved data referred to more extended surfaces.

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions.

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species-the bubblegum coral Paragorgia arborea-in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.

New High-Tech Flexible Networks for the Monitoring of Deep-Sea Ecosystems.

Increasing interest in the acquisition of biotic and abiotic resources from within the deep sea (e.g., fisheries, oil-gas extraction, and mining) urgently imposes the development of novel monitoring technologies, beyond the traditional vessel-assisted, time-consuming, high-cost sampling surveys. The implementation of permanent networks of seabed and water-column-cabled (fixed) and docked mobile platforms is presently enforced, to cooperatively measure biological features and environmental (physicochemical) parameters. Video and acoustic (i.e., optoacoustic) imaging are becoming central approaches for studying benthic fauna (e.g., quantifying species presence, behavior, and trophic interactions) in a remote, continuous, and prolonged fashion. Imaging is also being complemented by in situ environmental-DNA sequencing technologies, allowing the traceability of a wide range of organisms (including prokaryotes) beyond the reach of optoacoustic tools. Here, we describe the different fixed and mobile platforms of those benthic and pelagic monitoring networks, proposing at the same time an innovative roadmap for the automated computing of hierarchical ecological information on deep-sea ecosystems (i.e., from single species' abundance and life traits to community composition, and overall biodiversity).

A review on blockchain applications in the agri-food sector.

BACKGROUND: Food security can benefit from the technology's transparency, relatively low transaction costs and instantaneous applications. A blockchain is a distributed database of records in the form of encrypted blocks, or a public ledger of all transactions or digital events that have been executed and shared among participating parties and can be verified at any time in the future. Generally, the robust and decentralized functionality of the blockchain is used for global financial systems, but it can easily be expanded to contracts and operations such as tracking of the global supply chain. In the precision agriculture context, Information and Communications Technology can be further implemented with a blockchain infrastructure to enable new farm systems and e-agriculture schemes. RESULTS: The purpose of this review is to show a panorama of the scientific studies (enriched by a terms mapping analysis) on the use of blockchain in the agri-food sector, from both an entirely computational and an applicative point of view. As evidenced by the network analysis, the reviewed studies mainly focused on software aspects (e.g. the architecture and smart contracts). However, some aspects regarding the different blockchain knots (computers always connected to the blockchain network) having the role to store and distribute an updated copy of each block in a food supply-chain, result of crucial importance. CONCLUSION: These technologies appear very promising and rich of great potential showing a good flexibility for applications in several sectors but still immature and hard to apply due to their complexity. © 2019 Society of Chemical Industry.

A Blockchain Implementation Prototype for the Electronic Open Source Traceability of Wood along the Whole Supply Chain.

This is the first work to introduce the use of blockchain technology for the electronic traceability of wood from standing tree to final user. Infotracing integrates the information related to the product quality with those related to the traceability [physical and digital documents (Radio Frequency IDentification-RFID-architecture)] within an online information system whose steps (transactions) can be made safe to evidence of alteration through the blockchain. This is a decentralized and distributed ledger that keeps records of digital transactions in such a way that makes them accessible and visible to multiple participants in a network while keeping them secure without the need of a centralized certification organism. This work implements a blockchain architecture within the wood chain electronic traceability. The infotracing system is based on RFID sensors and open source technology. The entire forest wood supply chain was simulated from standing trees to the final product passing through tree cutting and sawmill process. Different kinds of Internet of Things (IoT) open source devices and tags were used, and a specific app aiming the forest operations was engineered to collect and store in a centralized database information (e.g., species, date, position, dendrometric and commercial information).

Comparison of six disease severity scores for allergic rhinitis against pollen counts a prospective analysis at population and individual level.

BACKGROUND: Many different symptom (medication) scores are nowadays used as measures of allergic rhinoconjunctivitis severity in individual patients and in clinical trials. Their differences contribute to the heterogeneity of the primary end-point in meta-analyses, so that calls for symptom (medication) score harmonization have been launched. OBJECTIVE: To prospectively compare six different severity scores for allergic rhinitis (AR) against pollen counts at both population and individual levels. METHODS: Two groups of children with seasonal AR and grass pollen sensitization were recruited in Ascoli, Italy (n = 76) and Berlin, Germany (n = 29). Symptoms and drug intake were monitored daily for 40 and 30 days of the grass pollen season in 2011 (Ascoli) and 2013 (Berlin), respectively, through an Internet-based platform (AllergyMonitor(™) , TPS Production srl, Rome, Italy). From the gathered data, the informatics platform automatically generated one symptom score (RTSS) and five symptom-medication scores (RC-ACS(©) , ACS, RTSS[LOCF], RTSS[WC] and AdSS). Values were then statistically normalized for reciprocal comparison and matched against the daily variations of local grass pollen counts (Spearman's rank correlation). RESULTS: The grass pollen counts were higher in Ascoli than in Berlin (peak values 194 vs. 59 grains/m(3) ). At population level, the trajectories of the normalized average values of the six scores differed only slightly in both studies and correlated well with the pollen counts (ranges r(2) : 0.38-0.50 in Ascoli, 0.41-0.56 in Berlin). By contrast, in individual patients, trajectories of different scores were often quite heterogeneous. The RTSS[WC] had a very low discriminatory power and generated in many patients long, flat horizontal segments. CONCLUSIONS: Disease severity scores for seasonal AR, as evaluated via an Internet-based platform, tend to provide similar results at population level but can often produce heterogeneous slopes in individual patients. The choice of the disease severity score might have only a low impact on the outcome of a very large clinical trial, but it may be crucial in the management of individual patients.

Class-modeling approach to PTR-TOFMS data: a peppers case study.

BACKGROUND: Proton transfer reaction-mass spectrometry (PTR-MS), in its recently developed implementation based on time-of-flight mass spectrometry (PTR-TOFMS), was used to rapidly determine the volatile compounds present in fruits of Capsicum spp. RESULTS: We analyzed the volatile organic compounds emission profile of freshly cut chili peppers belonging to three species and 33 different cultivars. PTR-TOFMS data, analyzed with appropriate and advanced multivariate class-modeling approaches, perfectly discriminated among the three species (100% correct classification in validation set). VIP (variable importance in projection) scores were used to select the 15 most important volatile compounds in discriminating the species. The best candidates for Capsicum spp. were compounds with measured m/z of 63.027, 101.096 and 107.050, which were, respectively, tentatively identified as dimethyl sulfide, hexanal and benzaldehyde. CONCLUSIONS: Based on the promising results, the possibility of introducing multivariate class-modeling techniques, different from the classification approaches, in the field of volatile compounds analyses is discussed.

Influence of the Drying Process on the Volatile Profile of Different Capsicum Species.

Chili is a globally significant spice used fresh or dried for culinary, condiment, and medicinal purposes. Growing concerns about food safety have increased the demand for high-quality products and non-invasive tools for quality control like origin tracing and safety assurance. Volatile analysis offers a rapid, comprehensive, and safe method for characterizing various food products. Thus, this study aims to assess the impact of the drying process on the aromatic composition of various Capsicum species and to identify key compounds driving the aromatic complexity of each genetic makeup. To accomplish these objectives, the aroma was examined in fruits collected from 19 different pepper accessions (Capsicum sp.) belonging to four species: one ancestral (C. chacoense) and three domesticated pepper species (C. annuum, C. baccatum and C. chinense). Fresh and dried samples were analyzed using a headspace PTR-TOF-MS platform. Our findings reveal significant changes in the composition and concentration of volatile organic compounds (VOCs) from fresh to dried Capsicum. Notably, chili peppers of the species C. chinense consistently exhibited higher emission intensity and a more complex aroma compared to other species (both fresh and dried). Overall, the data clearly demonstrate that the drying process generally leads to a reduction in the intensity and complexity of the aromatic compounds emitted. Specifically, fresh peppers showed higher volatile organic compounds content compared to dried ones, except for the two sweet peppers studied, which exhibited the opposite behavior. Our analysis underscores the variability in the effect of drying on volatile compound composition among different pepper species and even among different cultivars, highlighting key compounds that could facilitate species classification in dried powder. This research serves as a preliminary guide for promoting the utilization of various pepper species and cultivars as powder, enhancing product valorization.

Marine Science Can Contribute to the Search for Extra-Terrestrial Life.

Life on our planet likely evolved in the ocean, and thus exo-oceans are key habitats to search for extraterrestrial life. We conducted a data-driven bibliographic survey on the astrobiology literature to identify emerging research trends with marine science for future synergies in the exploration for extraterrestrial life in exo-oceans. Based on search queries, we identified 2592 published items since 1963. The current literature falls into three major groups of terms focusing on (1) the search for life on Mars, (2) astrobiology within our Solar System with reference to icy moons and their exo-oceans, and (3) astronomical and biological parameters for planetary habitability. We also identified that the most prominent research keywords form three key-groups focusing on (1) using terrestrial environments as proxies for Martian environments, centred on extremophiles and biosignatures, (2) habitable zones outside of "Goldilocks" orbital ranges, centred on ice planets, and (3) the atmosphere, magnetic field, and geology in relation to planets' habitable conditions, centred on water-based oceans.

A new colorimetrically-calibrated automated video-imaging protocol for day-night fish counting at the OBSEA coastal cabled observatory.

Field measurements of the swimming activity rhythms of fishes are scant due to the difficulty of counting individuals at a high frequency over a long period of time. Cabled observatory video monitoring allows such a sampling at a high frequency over unlimited periods of time. Unfortunately, automation for the extraction of biological information (i.e., animals' visual counts per unit of time) is still a major bottleneck. In this study, we describe a new automated video-imaging protocol for the 24-h continuous counting of fishes in colorimetrically calibrated time-lapse photographic outputs, taken by a shallow water (20 m depth) cabled video-platform, the OBSEA. The spectral reflectance value for each patch was measured between 400 to 700 nm and then converted into standard RGB, used as a reference for all subsequent calibrations. All the images were acquired within a standardized Region Of Interest (ROI), represented by a 2 × 2 m methacrylate panel, endowed with a 9-colour calibration chart, and calibrated using the recently implemented "3D Thin-Plate Spline" warping approach in order to numerically define color by its coordinates in n-dimensional space. That operation was repeated on a subset of images, 500 images as a training set, manually selected since acquired under optimum visibility conditions. All images plus those for the training set were ordered together through Principal Component Analysis allowing the selection of 614 images (67.6%) out of 908 as a total corresponding to 18 days (at 30 min frequency). The Roberts operator (used in image processing and computer vision for edge detection) was used to highlights regions of high spatial colour gradient corresponding to fishes' bodies. Time series in manual and visual counts were compared together for efficiency evaluation. Periodogram and waveform analysis outputs provided very similar results, although quantified parameters in relation to the strength of respective rhythms were different. Results indicate that automation efficiency is limited by optimum visibility conditions. Data sets from manual counting present the larger day-night fluctuations in comparison to those derived from automation. This comparison indicates that the automation protocol subestimate fish numbers but it is anyway suitable for the study of community activity rhythms.

Established and Emerging Research Trends in Norway Lobster, Nephrops norvegicus.

The burrowing crustacean decapod Nephrops norvegicus is a significant species in European Atlantic and Mediterranean fisheries. Research over the decades has mainly focused on behavioral and physiological aspects related to the burrowing lifestyle, since animals can only be captured by trawls when engaged in emergence on the seabed. Here, we performed a global bibliographic survey of all the scientific literature retrieved in SCOPUS since 1965, and terminology maps were produced with the VOSviewer software to reveal established and emerging research areas. We produced three term-map plots: term clustering, term citation, and term year. The term clustering network showed three clusters: fishery performance, assessment, and management; biological cycles in growth, reproduction, and behavior; and finally, physiology and ecotoxicology, including food products. The term citation map showed that intense research is developed on ecotoxicology and fishery management. Finally, the term year map showed that the species was first studied in its morphological and physiological aspects and more recently in relation to fishery and as a food resource. Taken together, the results indicate scarce knowledge on how burrowing behavior and its environmental control can alter stock assessment, because of the poor use of current and advanced monitoring technologies.

Distinguishing the Effects of Water Volumes versus Stocking Densities on the Skeletal Quality during the Pre-Ongrowing Phase of Gilthead Seabream (Sparus aurata).

Gilthead seabream (Sparus aurata) production is a highly valued aquaculture industry in Europe. The presence of skeletal deformities in farmed gilthead seabream represents a major bottleneck for the industry leading to economic losses, negative impacts on the consumers' perception of aquaculture, and animal welfare issues for the fish. Although past work has primarily focused on the hatchery phase to reduce the incidence of skeletal anomalies, this work targets the successive pre-ongrowing phase in which more severe anomalies affecting the external shape often arise. This work aimed to test the effects of: (i) larger and smaller tank volumes, stocked at the same density; and (ii) higher and lower stocking densities maintained in the same water volume, on the skeleton of gilthead seabream fingerlings reared for ~63 days at a pilot scale. Experimental rearing was conducted with gilthead seabream juveniles (~6.7 ± 2.5 g), which were selected as 'non-deformed' based on external inspection, stocked at three different densities (Low Density (LD): 5 kg/m3; Medium Density (MD): 10 kg/m3; High Density (HD): 20 kg/m3) in both 500 L and 1000 L tanks. Gilthead seabream were sampled for growth performance and radiographed to assess the skeletal elements at the beginning and end of the experimental trial. Results revealed that (i) LD fish were significantly longer than HD fish, although there were no differences in final weights, regardless of the water volume; (ii) an increase in the prevalence of seabream exhibiting cranial and vertebral axis anomalies was found to be associated with increased density. These results suggest that farmers can significantly reduce the presence of some cranial and axis anomalies affecting pre-ongrown gilthead seabream by reducing the stocking density.

RGB color calibration for quantitative image analysis: the "3D thin-plate spline" warping approach.

In the last years the need to numerically define color by its coordinates in n-dimensional space has increased strongly. Colorimetric calibration is fundamental in food processing and other biological disciplines to quantitatively compare samples' color during workflow with many devices. Several software programmes are available to perform standardized colorimetric procedures, but they are often too imprecise for scientific purposes. In this study, we applied the Thin-Plate Spline interpolation algorithm to calibrate colours in sRGB space (the corresponding Matlab code is reported in the Appendix). This was compared with other two approaches. The first is based on a commercial calibration system (ProfileMaker) and the second on a Partial Least Square analysis. Moreover, to explore device variability and resolution two different cameras were adopted and for each sensor, three consecutive pictures were acquired under four different light conditions. According to our results, the Thin-Plate Spline approach reported a very high efficiency of calibration allowing the possibility to create a revolution in the in-field applicative context of colour quantification not only in food sciences, but also in other biological disciplines. These results are of great importance for scientific color evaluation when lighting conditions are not controlled. Moreover, it allows the use of low cost instruments while still returning scientifically sound quantitative data.

Electronic nose application for determination of Penicillium digitatum in Valencia oranges.

BACKGROUND: Penicillium digitatum and Penicillium italicum are responsible for one the most serious diseases occurring during storage of citrus fruits. Its early detection allows a relevant increase in shelf life, and in situ monitoring of fungal infections represents a very efficient tool to improve storage quality. In the case of metabolic alterations due to physiological or fungal pathologies, olfactometric analysis allows the detection of specific volatile biomarkers, thus providing an effective tool for postharvest quality control of fruits and vegetables. RESULTS: A total of 300 Valencia oranges were analysed with an electronic nose and results were screened by a multivariate classification technique, partial least squares discriminant analysis, in order to investigate whether the electronic nose could distinguish between Penicillium-infected and non-infected samples and to evaluate the efficiency of the group classifications. High percentages of correct classification were obtained at low levels of infection (100% for 2-5% infection in an independent test). CONCLUSION: The electronic nose may be successfully applied as a reliable, non-destructive and non-contact indirect technology for the identification of fungal strains in storage rooms, especially when the infection occurs in small percentages that are not easily identifiable by classic methodologies of inspection.